Information Retrieval Basics: Boolean and Vector Space Models

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Explores text retrieval systems, inverted files, addressing granularity, and access structures in information retrieval.

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Introduces the basics of text data processing, covering document retrieval, classification, sentiment analysis, and topic detection.

Text Data Analysis: Techniques and Applications

Explores handling text data, deriving clean datasets from unstructured text, and various text analysis techniques.

Text-Based Information Retrieval

Covers the basic concepts of text-based information retrieval and how documents are indexed and retrieved based on user queries.

Python Programming: File Handling and Exceptions

Explores file handling and exceptions in Python programming, covering reading, writing, and error handling strategies.

Information Retrieval Basics: Document Length and Normalization

Explores document length, normalization, bias compensation, and retrieval model evaluation in information retrieval.

Handling Text: Document Retrieval, Classification, Sentiment Analysis

Explores document retrieval, classification, sentiment analysis, TF-IDF matrices, nearest-neighbor methods, matrix factorization, regularization, LDA, contextualized word vectors, and BERT.

Python: Dictionaries and Tuples

Explores dictionaries, tuples, mutable objects, and variable-length arguments in Python.

Python Basics: Syntax, Variables, Functions

Covers Python basics such as syntax, variables, and functions, introducing the Renku platform for collaborative data science.

Python Object Exercises

Covers Python object exercises with basic syntax questions and practical quizzes.